{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-08T10:19:01.340096Z",
     "start_time": "2025-04-08T10:19:01.337584Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "(3, 2)\n"
     ]
    }
   ],
   "source": [
    "#  ndarray.ndim 维度数\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "arr = np.array([[1,2],[3,4],[4,5]])\n",
    "print(arr.ndim)\n",
    "print(arr.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-08T10:19:10.725590Z",
     "start_time": "2025-04-08T10:19:10.723245Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zhu\n"
     ]
    }
   ],
   "source": [
    "print(\"zhu\")"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "conda-base-py",
   "language": "python",
   "display_name": "Python [conda env:base] *"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
